Clarifying the PSA grey zone: The management of patients with a borderline PSA

Proteome-wide mendelian randomization study implicates therapeutic targets in common cancers

BACKGROUND

The interest in targeted cancer therapies has been growing rapidly. While numerous cancer biomarkers and targeted treatment strategies have been developed and employed, there are still significant limitations and challenges in the early diagnosis and targeted treatment of cancers. Accordingly, there is an urgent need to identify novel targets and develop new targeted drugs.

METHODS

The study was conducted using combined cis-Mendelian randomization (cis-MR) and colocalization analysis. We analyzed data from 732 plasma proteins to identify potential drug targets associated with eight site-specific cancers. These findings were further validated using the UK Biobank dataset. Then, a protein-protein interaction network was also constructed to examine the interplay between the identified proteins and the targets of existing cancer medications.

RESULTS

This MR analysis revealed associations between five plasma proteins and prostate cancer, five with breast cancer, and three with lung cancer. Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. Our study indicatied that genetically predicted KDELC2 (OR: 0.89, 95% CI 0.86-0.93) and TNFRSF10B (OR: 0.74, 95% CI 0.65-0.83) are inversely associated with prostate cancer. Furthermore, we observed an inverse association between CPNE1 (OR: 0.96, 95% CI 0.94-0.98) and breast cancer, while PDIA3 (OR: 1.19, 95% CI 1.10-1.30) were found to be associated with the risk of breast cancer. In addition, we also propose that SPINT2 (OR: 1.05, 95% CI 1.03-1.06), GSTP1 (OR: 0.82, 95% CI 0.74-0.90), and CTSS (OR: 0.91, 95% CI 0.88-0.95) may serve as potential therapeutic targets in prostate cancer. Similarly, GDI2 (OR: 0.85, 95% CI 0.80-0.91), ISLR2 (OR: 0.87, 95% CI 0.82-0.93), and CTSF (OR: 1.14, 95% CI 1.08-1.21) could potentially be targets for breast cancer. Additionally, we identified SFTPB (OR: 0.93, 95% CI 0.91-0.95), ICAM5 (OR: 0.95, 95% CI 0.93-0.97), and FLRT3 (OR: 1.10, 95% CI 1.05-1.15) as potential targets for lung cancer. Notably, TNFRSF10B, GSTP1, and PDIA3 were found to interact with the target proteins of current medications used in prostate or breast cancer treatment.

CONCLUSIONS

This comprehensive analysis has highlighted thirteen plasma proteins with potential roles in three site-specific cancers. Continued research in this area may reveal their therapeutic potential, particularly KDELC2, TNFRSF10B, CPNE1, and PDIA3, paving the way for more effective cancer treatments.

A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation

OBJECTIVE

Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-based prediction model with minimized parameters and missing value handling algorithms for PC and clinically significant PC (CSPC).

MATERIALS AND METHODS

We retrospectively analyzed data from 18 824 prostate biopsies collected between March 2003 and December 2020 from 2 databases, resulting in 12 739 cases in the PSA gray zone of 2.0-10.0 ng/mL. Dense neural network (DNN) and extreme gradient boosting (XGBoost) models for PC and CSPC were developed with 5-fold cross-validation. The area under the curve of the receiver operating characteristic (AUROC) was compared with that of serum PSA, PSA density, free PSA (fPSA) portion, and prostate health index (PHI).

RESULTS

The AUROC values in the DNN model with the imputation of missing values were 0.739 and 0.708 (PC) and 0.769 and 0.742 (CSPC) in internal and external validation, whereas those of the non-imputed dataset were 0.740 and 0.771 (PC) and 0.807 and 0.771 (CSPC), respectively. The performance of the DNN model was like that of the XGBoost model, but better than all tested clinical biomarkers for both PC and CSPC. The developed DNN model outperformed PHI, serum PSA, and percent-fPSA with or without missing value imputation.

DISCUSSION

DNN models for missing value imputation can be used to predict PC and CSPC. Further validation in real-life scenarios are need to recommend for actual implementation, but the results from our study support the increasing role of deep learning analytics in the clinical setting.

CONCLUSIONS

A deep learning model for PC and CSPC in PSA gray zones using minimal, routinely used clinical parameter variables and data imputation of missing values was successfully developed and validated.

Role of glutathione-S-transferase gene P1 in the diagnosis of prostate cancer in patients with 'grey level' prostate-specific antigen values

Prostate cancer (PC) represents the second most frequent cancer diagnosis in men and, at the same time, is one of the top six causes of death worldwide. The aim of the present study was to evaluate the diagnostic value of glutathione-S-transferase gene P1 (GST-P1) in patients that fall within the 'grey area' of the prostate-specific antigen (PSA) values. A retrospective observational study on 80 patients with prostate abnormal volumes and PSA values in the range 4-10 ng/ml was performed. The prostate gland was extracted following transrectal ultrasonography, and GST-P1 gene expression was analysed. A histopathological examination was considered the gold standard for PC diagnosis. Among the 53 patients diagnosed with PC, 69.8% (n=37) were GST-P1-positive, whereas, among the 27 patients diagnosed with benign prostatic hyperplasia, 18.5% (n=5) were GST-P1-positive. The sensitivity for diagnosing PC in patients with PSA values between 4 and 10 ng/ml was 69.81%, and the specificity was 81.48%. The positive predictive value was 88.1% [95% confidence interval (CI), 74.37-96.02%] and the negative predictive value was 57.89% (95% CI, 40.82-73.69%). Collectively, these results show the potential of using GST-P1 gene expression in patients who are suspected of having PC, but where the PSA values are inconclusive.

MiRNA biomarkers in cancers of the male reproductive system: are we approaching clinical application?

BACKGROUND

Specific cancer types face specific clinical management challenges. Owing to their stability, robustness and fast, easy, and cost-effective detection, microRNAs (miRNAs) are attractive candidate biomarkers to the clinic.

OBJECTIVES

Based on a comprehensive review of the relevant literature in the field, we explore the potential of miRNAs as biomarkers to answer relevant clinical dilemmas inherent to cancers of the male reproductive tract (prostate (PCa), testis (TGCTs) and penis (PeCa)) and identify some of the challenges/limitations hampering their widely application.

RESULTS AND DISCUSSION

We conclude that the use of miRNAs as biomarkers is at different stages for these distinct cancer types. While for TGCTs, miRNA-371a-3p is universally accepted to fill in important clinicals gaps and is moving fast towards clinical implementation, for PCa almost no overlap of miRNAs exists between studies, denoting the absence of a consistent miRNA biomarker, and for PeCa the field of miRNAs has just recently started, with only a few studies attempting to explore their clinical usefulness.

CONCLUSION

Technological advances influencing miRNA detection and quantification will be instrumental to continue to move forward with implementation of miRNAs in the clinic as biomarkers for non-invasive diagnosis, risk stratification, treatment monitoring and follow-up. This article is protected by copyright. All rights reserved.

Diagnostic accuracy of prostate-specific antigen below 4 ng/mL as a cutoff for diagnosing prostate cancer in a hospital setting: A systematic review and meta-analysis

PURPOSE

A prostate-specific antigen (PSA) cutoff of 4 ng/mL has been widely used for prostate cancer screening in population-based settings. However, the accuracy of PSA below 4 ng/mL as a cutoff for diagnosing prostate cancer in a hospital setting is inconclusive. We systematically reviewed the accuracy of PSA below 4 ng/mL cutoff in a hospital setting.

MATERIALS AND METHODS

We systematically reviewed the literature by searching major databases until March 2020, and a meta-analysis and quality assessment were performed.

RESULTS

A total of 11 studies were included at the completion of the screening process. The meta-analysis showed a sensitivity of 0.92 and a specificity of 0.16 for a PSA cutoff below 4 ng/mL. The area under the hierarchical summary receiver operating characteristic curve was 0.87, the positive likelihood ratio was 1.23, the negative likelihood ratio was 0.46, and the diagnostic odds ratio was 2.64. PSA sensitivities and specificities varied according to the cutoff range: 0.94 and 0.17 for 2 to 2.99 ng/mL, and 0.92 and 0.16 for 3 to 3.99 ng/mL, respectively. No significant differences in the sensitivity and specificity of PSA cutoffs in the range of 2 to 2.99 ng/mL and 3 to 3.99 ng/mL were found.

CONCLUSIONS

Although a PSA cutoff <3 ng/mL is relatively more sensitive and specific than PSA ≥3 ng/mL, no significant differences in sensitivity and specificity were found in the diagnosis of prostate cancer. Therefore, clinicians should choose an appropriate PSA cutoff on the basis of clinical circumstances and patients' characteristics.

Dysregulation of the miRNome unveils a crosstalk between obesity and prostate cancer: miR-107 asa personalized diagnostic and therapeutic tool

Prostate-specific antigen (PSA) is the gold-standard marker to screen prostate cancer (PCa) nowadays. Unfortunately, its lack of specificity and sensitivity makes the identification of novel tools to diagnose PCa an urgent medical need. In this context, microRNAs (miRNAs) have emerged as potential sources of non-invasive diagnostic biomarkers in several pathologies. Therefore, this study was aimed at assessing for the first time the dysregulation of the whole plasma miRNome in PCa patients and its putative implication in PCa from a personalized perspective (i.e., obesity condition). Plasma miRNome from a discovery cohort (18 controls and 19 PCa patients) was determined using an Affymetrix-miRNA array, showing that the expression of 104 miRNAs was significantly altered, wherein six exhibited a significant receiver operating characteristic (ROC) curve to distinguish between control and PCa patients (area under the curve [AUC] = 1). Then, a systematic validation using an independent cohort (135 controls and 160 PCa patients) demonstrated that miR-107 was the most profoundly altered miRNA in PCa (AUC = 0.75). Moreover, miR-107 levels significantly outperformed the ability of PSA to distinguish between control and PCa patients and correlated with relevant clinical parameters (i.e., PSA). These differences were more pronounced when considering only obese patients (BMI > 30). Interestingly, miR-107 levels were reduced in PCa tissues versus non-tumor tissues (n = 84) and in PCa cell lines versus non-tumor cells. In vitro miR-107 overexpression altered key aggressiveness features in PCa cells (i.e., proliferation, migration, and tumorospheres formation) and modulated the expression of important genes involved in PCa pathophysiology (i.e., lipid metabolism [i.e., FASN] and splicing process). Altogether, miR-107 might represent a novel and useful personalized diagnostic and prognostic biomarker and a potential therapeutic tool in PCa, especially in obese patients.

Two Novel Ceramide-Like Molecules and miR-5100 Levels as Biomarkers Improve Prediction of Prostate Cancer in Gray-Zone PSA

Reliable liquid biopsy-based tools able to accurately discriminate prostate cancer (PCa) from benign prostatic hyperplasia (BPH), when PSA is within the “gray zone” (PSA 4–10), are still urgent. We analyzed plasma samples from a cohort of 102 consecutively recruited patients with PSA levels between 4 and 16 ng/ml, using the SANIST-Cloud Ion Mobility Metabolomic Mass Spectrometry platform, combined with the analysis of a panel of circulating microRNAs (miR). By coupling CIMS ion mobility technology with SANIST, we were able to reveal three new structures among the most differentially expressed metabolites in PCa vs. BPH. In particular, two were classified as polyunsaturated ceramide ester-like and one as polysaturated glycerol ester-like. Penalized logistic regression was applied to build a model to predict PCa, using six circulating miR, seven circulating metabolites, and demographic/clinical variables, as covariates. Four circulating metabolites, miR-5100, and age were selected by the model, and the corresponding prediction score gave an AUC of 0.76 (C.I. = 0.66–0.85). At a specified cut-off, no high-risk tumor was misclassified, and 22 out of 53 BPH were correctly identified, reducing by 40% the false positives of PSA. We developed and applied a novel, minimally invasive, liquid biopsy-based powerful tool to characterize novel metabolites and identified new potential non-invasive biomarkers to better predict PCa, when PSA is uninformative as a tool for precision medicine in genitourinary cancers.

In1-Ghrelin Splicing Variant as a Key Element in the Pathophysiological Association Between Obesity and Prostate Cancer

CONTEXT

Recent studies emphasize the importance of considering the metabolic status to develop personalized medicine approaches. This is especially relevant in prostate cancer (PCa), wherein the diagnostic capability of prostate-specific antigen (PSA) dramatically drops when considering patients with PSA levels ranging from 3 to 10 ng/mL, the so-called grey zone. Hence, additional noninvasive diagnostic and/or prognostic PCa biomarkers are urgently needed, especially in the metabolic-status context.

OBJECTIVE

To assess the potential relation of urine In1-ghrelin (a ghrelin-splicing variant) levels with metabolic-related/pathological conditions (eg, obesity, diabetes, body mass index, insulin and glucose levels) and to define its potential clinical value in PCa (diagnostic/prognostic capacity) and relationship with PCa risk in patients with PSA in the grey zone.

METHODS

Urine In1-ghrelin levels were measured by radioimmunoassay in a clinically, metabolically, pathologically well-characterized cohort of patients without (n = 397) and with (n = 213) PCa with PSA in the grey zone.

RESULTS

Key obesity-related factors associated with PCa risk (BMI, diabetes, glucose and insulin levels) were strongly correlated to In1-ghrelin levels. Importantly, In1-ghrelin levels were higher in PCa patients compared to control patients with suspect of PCa but negative biopsy). Moreover, high In1-ghrelin levels were associated with increased PCa risk and linked to PCa aggressiveness (eg, tumor stage, lymphovascular invasion). In1-ghrelin levels added significant diagnostic value to a clinical model consisting of age, suspicious digital rectal exam, previous biopsy, and PSA levels. Furthermore, a multivariate model consisting of clinical and metabolic variables, including In1-ghrelin levels, showed high specificity and sensitivity to diagnose PCa (area under the receiver operating characteristic curve = 0.740).

CONCLUSIONS

Urine In1-ghrelin levels are associated with obesity-related factors and PCa risk and aggressiveness and could represent a novel and valuable noninvasive PCa biomarker, as well as a potential link in the pathophysiological relationship between obesity and PCa.

Relevance of Circulating Nucleosomes, HMGB1 and sRAGE for Prostate Cancer Diagnosis

BACKGROUND/AIM

Evasion from cell death occurs in prostate cancer (PCa). We verified whether serum levels of cell death markers can have diagnostic value in PCa.

PATIENTS AND METHODS

A total of 233 men scheduled for prostate biopsy [prostate specific antigen (PSA) level: 2-10 ng/ml] were enrolled. Serum nucleosomes, nucleosomes containing the H3 histone (H3), high mobility group box 1 (HMGB1), and soluble receptor for advanced glycation end products (sRAGE) were analyzed by enzyme immunoassays.

RESULTS

There were no differences (p>0.05) in nucleosomes, H3, and sRAGE levels between patients with and without PCa or clinically significant PCa (csPCa). HMGB1 had lower levels in PCa patients (p=0.023) and was a predictor of PCa (p=0.047), but not of csPCa (p=0.180).

CONCLUSION

In patients with critical PSA levels between 2-10 ng/ml, HMGB1 had some diagnostic value for overall PCa detection, but it was not predictive of csPCa. Nucleosomes, H3 and sRAGE did not discriminate between PCa or csPCa and controls.

Spotlight on PSMA as a new theranostic biomarker for bladder cancer

Bladder cancer (BCa) patients are diagnosed by cytology and cystoscopy. However, these diagnostic tests bear some limitations. We sought for reliable biomarkers to better determine BCa extension. Prostate-specific membrane antigen (PSMA) appears to fulfill this requirement in prostate cancer but its role in BCa has not been established yet. We then analyzed 87 bladder tissue samples from 74 patients assessing PSMA expression by immunohistochemistry. The median PSMA expression, exclusively found in tumor neovasculature, in terms of H-score significantly differed between non-tumor samples and tumor samples (p = 0.00288) showing a higher neovasculature-related PSMA expression. No differences were observed in relation to tumor type, grade and stage. BCa neovasculature-related PSMA overexpression may be useful in defining the degree of extension of the neoplasm. In addition, testing PSMA expression by immunohistochemistry may hold theranostic implications both considering anti-angiogenesis agents and radio-labelled PSMA ligands for intracavitary radionuclide therapy. In our opinion, BCa neovasculature-related PSMA overexpression may be considered an apt target for anti-angiogenesis and radionuclide treatment in BCa, once the evaluation of tumor-retention time for the appropriateness of long half-life therapeutic PSMA ligands as radionuclide treatment will be performed.

Blood-Derived Biomarkers of Diagnosis, Prognosis and Therapy Response in Prostate Cancer Patients

Prostate cancer is among the most frequent cancers in men worldwide. Despite the fact that multiple therapeutic alternatives are available for its treatment, it is often discovered in an advanced stage as a metastatic disease. Prostate cancer screening is based on physical examination of prostate size and prostate-specific antigen (PSA) level in the blood as well as biopsy in suspect cases. However, these markers often fail to correctly identify the presence of cancer, or their positivity might lead to overdiagnosis and consequent overtreatment of an otherwise silent non-progressing disease. Moreover, these markers have very limited if any predictive value regarding therapy response or individual risk for therapy-related toxicities. Therefore, novel, optimally liquid biopsy-based (blood-derived) markers or marker panels are needed, which have better prognostic and predictive value than the ones currently used in the everyday routine. In this review the role of circulating tumour cells, extracellular vesicles and their microRNA content, as well as cellular and soluble immunological and inflammation- related blood markers for prostate cancer diagnosis, prognosis and prediction of therapy response is discussed. A special emphasis is placed on markers predicting response to radiotherapy and radiotherapy-related late side effects.

Profiling the proteoforms of urinary prostate-specific antigen by capillary electrophoresis - mass spectrometry

Early detection of prostate cancer may lead to the overdiagnosis and overtreatment of patients as well as missing significant cancers. The current diagnostic approach uses elevated serum concentrations of prostate-specific antigen (PSA) as an indicator of risk. However, this test has been widely criticized as it shows poor specificity and sensitivity. In order to improve early detection and diagnosis, several studies have investigated whether different PSA proteoforms are correlated to prostate cancer. Until now, studies and methodologies for the comprehensive characterization of PSA proteoforms from biofluids are scarce. For this purpose, we developed an intact protein assay to analyze PSA by capillary electrophoresis-electrospray ionization-mass spectrometry after affinity purification from patients' urine. Here, we determined six proteolytic cleavage variants. In regard to glycosylation, tri-, di-, mono- and non-sialylated complex-type N-glycans were found on non-cleaved PSA, as well as the non-glycosylated variant. The performance of the intact protein assay was assessed using a pooled sample, obtaining an inter-day variability of 15%. Furthermore, urinary patient samples were analyzed by intact protein analysis and a bottom-up approach (glycopeptide analysis). This combined approach revealed complimentary information on both levels, demonstrating the benefit of using two orthogonal techniques to provide a thorough profile of urinary PSA. SIGNIFICANCE: The detection of clinically relevant prostate cancer requires a more specific and sensitive biomarker and, in this case, several PSA proteoforms may be able to aid or improve the current PSA test. However, a comprehensive analysis of the intact PSA proteoform profile is still lacking. This study investigated the PSA proteoforms present in urine and, in particular, determined the relative contribution of cleaved PSA and non-cleaved PSA forms to the total glycosylation profile. Importantly, intact protein analysis did not require further sample treatment before being measured by CE-ESI-MS. Furthermore, its glycosylation was also assessed in a bottom-up approach to provide complementary information. Overall, these results represent an important basis for future characterization and biomarker studies.

Identification of Clinically Significant Prostate Cancer by Combined PCA3 and AMACR mRNA Detection in Urine Samples

Purpose

Preclinical evaluation of PCA3 and AMACR transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort.

Patients and Methods

We analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). PCA3, AMACR, PSA and GPI transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis.

Results

There was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy p=0.0088, prebiopsy cohort p=0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511–0.752), the AMACR score AUC was 0.711 (95%CI: 0.617–0.806) and AUC of classification model based on the PCA3 score, the AMACR score and total PSA was 0.72 (95%CI: 0.58–0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, p=6.542e–09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use.

Conclusion

The AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.

Ion Channel Profiling in Prostate Cancer: Toward Cell Population-Specific Screening

In the last three decades, a growing number of studies have implicated ion channels in all essential processes of prostate carcinogenesis, including cell proliferation, apoptosis, migration, and angiogenesis. The changes in the expression of individual ion channels show a specific profile, making these proteins promising clinical biomarkers that may enable better molecular subtyping of the disease and lead to more rapid and accurate clinical decision-making. Expression profiles and channel function are mainly based on the tumoral tissue itself, in this case, the epithelial cancer cell population. To date, little data on the ion channel profile of the cancerous prostate stroma are available, even though tumor interactions with the microenvironment are crucial in carcinogenesis and each distinct population plays a specific role in tumor progression. In this review, we describe ion channel expression profiles specific for the distinct cell population of the tumor microenvironment (stromal, endothelial, neuronal, and neuroendocrine cell populations) and the technical approaches used for efficient separation and screening of these cell populations.

Correlation of Prostate-Imaging Reporting and Data Scoring System scoring on multiparametric prostate magnetic resonance imaging with histopathological factors in radical prostatectomy material in Turkish prostate cancer patients: a multicenter study of the Urooncology Association

Background

Histopathological features after radical prostatectomy (RP) provide important information for the prognosis of prostate cancer (PCa). The possible correlations between Prostate-Imaging Reporting and Data Scoring System (PIRADS) scores in multiparametric magnetic resonance imaging (mpMRI) may also be predictive for prognosis. In this study, we aimed to evaluate the correlation of PIRADS scores with histopathological data.

Methods

A total of 177 patients who underwent preoperative mpMRI and RP for PCa from eight institutions were included in the study. Correlation of PIRADS score in preoperative mpMRI with adverse histopathological factors in RP specimen was investigated using univariate and multivariate analyses.

Results

The relationship between PIRADS score and postoperative extracapsular extension, lymphovascular invasion, and seminal vesicle involvement was significant (P < 0.001, P = 0.032, and P = 0.007, respectively). Although the PIRADS score was significantly correlated with the number of dissected lymph nodes (p = 0.026), it had no significant correlation with the number of positive nodes (P = 0.611). Total Gleason score, extracapsular extension, seminal vesicle invasion, and number of lymph nodes were found to be independent factors, which correlated with high PIRADS scores in ordinal logistic regression analysis.

Conclusion

PIRADS scoring system in mpMRI showed a statistically significant correlation with adverse histopathological factors in RP specimen. A higher PIRADS score may help to predict a higher Gleason score, indicating clinically important PCa as well as poor prognotic factors such as extracapsular extension, lymphovascular invasion, and seminal vesicle invasion that may indicate a higher risk of recurrence and the need for additional treatment.

A Comparative Study: Has MRI-guided Fusion Prostate Biopsy Changed the Prostate-specific Antigen Gray-zone Range?

Objective

The gray-zone prostate-specific antigen (PSA) range is accepted to be 4-10 ng/ml and is considered to vary according to age. We aimed to investigate whether fusion prostate biopsy (FPB), which has been reported to have relatively higher cancer detection rates, has an effect on gray-zone PSA cut-off value.

Material and methods

This retrospective study included patients that underwent standard prostate biopsy (SPB) or multiparametric magnetic resonance imaging (MpMRI)-guided FPB (SPB+ targeted biopsy). All the patients included in the study were detected with a Prostate Imaging Reporting and Data System (PI-RADS) ≥3 lesion on MpMRI (the FPB group only). The demographics, clinical characteristics, and histopathological diagnoses were recorded for each patient.

Results

A total of 1,628 patients comprising 1,208 patients in the SPB group and 420 patients in the FPB group were included in the study. The mean PSA level was 9.75±6.68 ng/ml in the FBP group and 10.46±6.46 ng/ml in the SPB group (p=0.053). Prostate cancer (PCa) detection rate was significantly higher in the FPB group as compared to the SPB group (42.4% vs. 36.4%). The PSA cut-off value for PCa was 9.75 ng/ml (sensitivity and specificity, 81%) in the SPB group and was 7.55 ng/ml (sensitivity and specificity, 81% and 84%, respectively) in the FPB group. In the FPB group, the cancer detection rate among the patients with a PSA level of 7.55-10.00 ng/ml was 56.1%.

Conclusion

The results indicated that the introduction of FPB into clinical practice, which has relatively higher cancer detection rates, has further lowered the upper limit for gray-zone PSA.

PSMA expression: a potential ally for the pathologist in prostate cancer diagnosis

Prostate cancer (PCa) patients are risk-stratified on the basis of clinical stage and PSA level at diagnosis and the Gleason Score (GS) in prostate biopsy. However, these parameters are not completely accurate in discriminating between high- and low-risk disease, creating a need for a reliable marker to determine aggressiveness. Prostate-specific membrane antigen (PSMA) appears to fulfill this need. We analyzed 79 prostate biopsies and 28 prostatectomies to assess whether PSMA expression detected by immunohistochemistry is related to GS. PSMA expression was correlated with GS in both sample types (biopsies, P < 0.0001 and prostatectomy samples, P = 0.007). We observed lower PSMA expression in Gleason pattern 3 than Gleason pattern 4, suggesting that this biomarker could be useful to distinguish between these entities (p < 0.0001). The best cut-off value of 45% immunopositivity was determined by receiver operating characteristic (ROC) curve analysis. In Gleason pattern 3 vs. Gleason pattern 4 and 5, PSMA sensitivity was 84.1% (95% CI 76.5%-91.7%) and specificity was 95.2% (95% CI 90.6%-99.8%), with an area under the curve of 93.1 (95% CI 88.8-97.4). Our results suggest that PSMA represents a potential ally for the pathologist in the diagnostic work-up of PCa to overcome long-standing morphological classification limits.